The original TS-VAD model was developed for the CHiME-6 challenge, where each recording consisted of exactly 4 speakers, and using this information was permissible for the participants. Since real-life scenarios may not always adhere to such restrictions, we used meeting-style recordings with varying numbers of speakers for our evaluation. Our evaluation data comprises 3 variants of the LibriCSS dataset [1]}, consisting of 2, 5, and 8 speakers, respectively. The dataset contains multi-channel audio recordings of “simulated conversations,” generated by mixing test utterances from Librispeech [2]}. There are 10 sessions, where each session is approximately one hour long and made up of six 10-minute-long “mini sessions” that have different overlap ratios (ranging from 0% to 40%). Here, overlap ratio refers to the the fraction of speaking time that contains overlapping speech. The audios were recorded in a regular meeting room using a seven-channel circular microphone array. We selected the first channel of the array for our experiments.
